Recognition of the potential for drug combinations has a long history in medicine. In the fields of pharmacology and toxicology, the concepts of synergy, chemical addition and antagonism have been explored, but without a concomitant development of successful pharmacological models that predict or explain these effects. Many doctors rely on combination therapies that have been proven successful as treatments for several diseases, but these combinations have not been rigorously optimized by any explicit scientific method. The heuristic testing of drug combinations is an accepted strategy for drug development and improvement without a concomitant understanding of the underlying scientific principles necessary for the discovery of optimal drug combinations. This has led to the heuristic and rather indiscriminate testing of combinations of drugs in patients as an explicit strategy for drug development and improvement.
Most of these new drug combinations are created from chemical agents already known to be effective for treating specific diseases or chronic conditions. Generally, combinations are created that seem intuitively beneficial because they have similar clinical endpoints; however, such combinations represent only a small fraction of the combinations possible and are unlikely to result in optimal therapeutics without knowing the pharmacological relationships between the drug concentration ratios and their corresponding therapeutic effects. In combination, two or more drugs may produce responses that are not at all similar to the activity of the individual components alone (similar to the way that colors can be combined to form another, distinct color such as by combining yellow and blue to make green). The biological effects of these combinations are not intuitively obvious even to those skilled in the arts of pharmacology, pharmacy, biotechnology or the pharmaceutical sciences. Since the variations in drug ratios within these combinations are relevant to finding the best therapeutic combinations, an efficient scientific method is needed to find these optimal therapeutic drug combinations.
In addition, one or more of the following mistakes are often made when creating or using various combination products:                1) Not making a specific ratio combination to test as a single combination product. This includes not pre-mixing and combining the individual components before testing.        2) Not testing the specific ratio combinations over the full range of the dose-response curve and comparing this to the dose-responses of the individual components alone.        
For example, by not making a single specific ratio combination, the effects of adding another molecule or drug on the dose-response curve may produce an effect on the receptor response that is not quantifiable if the ratio of the combination varies or is unknown over the course of the experiment. A second example concerns not pre-mixing the molecules with sufficient care, which may subject the receptors to initially be in contact with only one component or in contact with varying combination ratios of the combination product, which may cause spurious observations. As a final example, the specific ratio combination should be treated as if it is a new drug entity that should be tested over a full dose-response range and compared with the dose-response curves over the same range for its individual components. Previously, those who have studied drug combinations have not studied them for their utility over the full dose-response range. Not considering the full dose-response range may hide dosage effects that need to be accounted for in comparison to the single drug alone.
Each of the three mistakes listed above produces experimental observations that do not fully or truly characterize these specific ratio combination products. The advantages and disadvantages of these specific combinations may be missed by incomplete dose-response information such as testing at a single dose that may not reflect the clinical use of the drug over time. By not addressing these three mistakes, most previous experiments claiming to find or test new properties of specific combinations have not done so in a complete or rigorous scientific manner.
Several scientists attribute the untoward effects of combinations on the combined effect of both drugs on the complex signaling networks that coordinate activity within and between cells. It is thought that using drugs in combination interrupts or modulates these intracellular networks at multiple points and influences cellular signaling networks in ways that the individual components cannot.
Although recent efforts have examined the benefits of combinatorial drugs for treating various diseases, there remain problems in modeling, understanding and controlling primary and secondary effects from such combinations. Pharmacology has yet to discover optimal methods that sufficiently characterize these changes that are produced in receptor systems. Simulations of the biological receptor responses created by fitting somewhat arbitrary and awkward computer algorithms or models to interdependent biological networks produce results that demonstrate synergistic effects but have limited predictive or conceptual value. However, system biology models are useful for systematically recording, displaying or mining such observations for possible therapeutic benefits or potential drug side effects.
Although these models attribute the untoward effects of drug combinations on their cumulative effects on intracellular signaling and metabolic networks, there is also evidence that these complex and untoward effects are generated at the very earliest interactions of drugs or molecular ligands with their complementary cellular receptors. Independent experimental observations have supported the suggestion that receptors contain free sulfhydryl groups that can be modulated by other molecules such as ascorbate, which may influence the sulfhydryl oxidation/reduction equilibrium thereby creating more active receptors and making the experimental preparation more sensitive to stimulating drugs such as norepinephrine.
Often, it is these early events in molecular recognition that guide the subsequent downstream and intracellular responses. The fact that these early receptor-activation events remain poorly understood and uncharacterized in most of the models used in systems biology or bio-informatics reduces the inherent validity of these models. By focusing on the systems biology approach alone, secondary activation events may be artificially created for intracellular signaling pathways that may be inaccurate and unnecessary.
The earliest events of receptor activation require the recognition of an extracellular signal that usually involves an endogenous agonist ligand activating its target receptor. As general models for receptor activation, the G protein-coupled receptors (GPCRs) have been studied extensively to understand the complex molecular changes that accompany receptor activation and signal transduction. Recent experimental discoveries have significantly changed our understanding of how these receptors work. It has been demonstrated that transgenic mice with an increased number of B2AR receptors exhibit spontaneous activity similar to normally expressed receptors in the presence of an agonist ligand.
This observation separated receptor activation from the action of agonist ligands alone and prompted a revision of receptor models to include an intrinsically active receptor state. One consequence of this revision was that the resting populations of receptors must interconvert by themselves from resting to active states. However, the biophysical basis for these active and inactive receptor states has not been adequately defined or understood.
Models that describe receptor activation use various mathematical techniques to depict the mathematical relationships between key receptor and drug-receptor species. Those models that use differential equations to capture the dynamic changes within these systems, miss the overall net changes produced by selective ligand binding to the populations of receptor states or alternative factors that can change these states such as a change in receptor number and constitutive activity. Many of these expressions can be made to fit the available data, but are difficult to extrapolate to meaningful biophysical data that predict biological responses.
In general, two-state mathematical models have been recognized to be among the most successful for describing receptor activation. Most of these models calculate either the proportional or fractional receptor occupancy as the overall receptor response. Although it is seductive to assume that the proportional amount of an active receptor state should correlate with the biological response, the experimental evidence for receptor overexpression and spare receptors suggests that the net change in the active receptor state is a much better measure for response than is the fractional or proportional change. This is also demonstrated by the experimental observations that agonist/antagonist combinations can reduce or prevent the desensitization of beta-receptors, which is not predicted by other models.
This is also demonstrated by receptors that are activated by overexpression since this requires a change between R and R* that is difficult to understand in terms of a proportional rather than a net change. One possible perspective is that there exists an initial equilibrium between the inactive and active receptor states that is perturbed by ligand binding to produce a shift in the net amounts of these states. An agonist ligand favoring the active receptor state perturbs the initial chemical equilibrium toward the higher affinity receptor state, thereby inducing receptor activation in a manner similar to Le Chatelier's principle.
From this perspective, it is important to determine within the constructs of any biophysical model what molecular states interconvert either by ligand stimulation, receptor overexpression, mutations or other modulating molecules or drugs. The model presented herein calculates this net change as a distinct parameter with biophysical parameters that have direct mathematical relationships to recognizable molecular receptor states. This model is the only one that takes this approach toward receptor activation. Those models that do not parameterize for a net change and use inappropriate or unrealistic biophysical parameters have difficulties in quantifying pharmacological responses in a meaningful way.
In parallel to the systems biology approach, recent developments in pharmacology have provided new insights into a variety of modulating signaling molecules or drugs that produce varied interactions with their targeted receptors, which, in turn, create a wide range of intracellular responses. Many if not most of these different signaling pathways are initiated by the earliest differences in ligand-induced intermediate molecular conformational states produced when ligand molecules bind to their target receptors, as shown for the beta-2-adrenergic receptor and for the 5-HT2A receptor. Other possible mechanisms may include the promiscuity of the receptors' interactions with a diversity of G proteins and other signaling partner proteins that couple with these receptors as well as receptor structural changes that include phosphorylation, palmitoylation, glycosylation, thiol modification, ubiquitination and/or oligomer formation.
These intracellular responses may include, but may not be limited to, the alteration of kinase/phosphatase activities, arrestin binding or unbinding, ubiquitination, phospholipase activities, methylation/demethylation, the modification of post-translational proteins and various peptidase activities as well as many other intracellular signaling cascades or enzyme reactions too numerous to mention here. The alteration of these intracellular response pathways by externally acting molecules and drugs often activate multiple intracellular effects such as an enzyme together with a kinase that subsequently phosphorylates other proteins within the cell. These signaling molecules include those labeled as “protean agonists” and those that demonstrate “functional selectivity” in their varied sets of receptor responses. Such studies have recently led to a redefinition of the concept of efficacy such that ligands can produce multiple stimuli (have multiple intrinsic efficacies) upon interaction with a receptor and can differentially regulate each of multiple signaling pathways coupled to a receptor. This ligand behavior has been termed “protean agonism”, “agonist-directed trafficking of receptor stimulus”, “functional selectivity”, “conformational cafeteria”, “pleiotropy”, “stimulus trafficking”, and “biased agonism”. The underlying mechanism for this is proposed to be based upon the capacity of ligands to promote unique, ligand-selective receptor conformations that have differential efficacy to regulate signal transduction pathways.
Many important bio-pharmaceutical molecules or drugs differentially activate signaling pathways mediated by the G protein coupled receptors (GPCRs) and several other cellular receptors, such as the ion channel linked or ionotropic receptors, the tyrosine kinase and toll-like receptors. Experimental data illustrating these phenomena are known from the serotonin, angiotensin, vasopressin, adrenergic, opioid and dopamine receptor systems among others. Functionally selective ligands may often produce secondary responses due to cross receptor and intracellular effector signaling pathway stimulation, or due to the allosteric effects that some pharmaceutical molecules demonstrate by activating, modulating or otherwise altering other cellular targets or sites. The principles of allosterism in reference to drug action are important in the ionotropic and G-protein coupled receptor systems, which encompass the GABAA, GABAB, 5HT3, nicotinic, ionotropic and metabotropic receptors for glutamate, muscarinic and alpha 2 adrenergic receptors. As these effects become better characterized with meaningful biophysical parameters, a suitable method to account for these effects is important in order to design an optimal technique to minimize, reduce and/or block those effects that may be detrimental to the overall therapeutic response of many important bio-pharmaceutical molecules or drugs.